5 Commits (8ceceef889f69a30ffe5c8ef45140a8492a38ddb)

Author	SHA1	Message	Date
Ross Wightman	76539d905e	Some transform/data/loader refactoring, hopefully didn't break things ... * factor out data related constants to own file * move data related config helpers to own file * add a variant of RandomResizeCrop that randomizes interpolation method * remove old Numpy version of RandomErasing * cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform	5 years ago
Ross Wightman	0562b91c38	Add per model crop pct, interpolation defaults, tie it all together ... * create one resolve fn to pull together model defaults + cmd line args * update attribution comments in some models * test update train/validation/inference scripts	6 years ago
Ross Wightman	9c3859fb9c	Uniform pretrained model handling. ... * All models have 'default_cfgs' dict * load/resume/pretrained helpers factored out * pretrained load operates on state_dict based on default_cfg * test all models in validate * schedule, optim factor factored out * test time pool wrapper applied based on default_cfg	6 years ago
Ross Wightman	0bc50e84f8	Lots of refactoring and cleanup. ... * Move 'test time pool' to Module that can be used by any model, remove from DPN * Remove ResNext model file and combine with ResNet * Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count * Cleanup adaptive avgmax pooling and add back conctat variant * Factor out checkpoint load fn	6 years ago
Ross Wightman	5855b07ae0	Initial commit, puting some ol pieces together	6 years ago